financial risk meter

Current Risk Level:



Interactive moving time window: select desired frame in lower graph.

What is this about:

The Financial Risk Meter (FRM) helps you to identify different systemic risk level in the financial market over time. It is an index of the system volatility level which indicates that if FRM is high, then the systemic risk is high.


We propose a linear lasso measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in an ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting for a linear quantile regression framework with 5% quantile. We can thus include more financial institutions into the analysis, to measure their interdependencies in tails.

Then FRM is induced from this model which is the averaged tuning parameter lambda from lasso technique. The estimation method of it is cross validation. In application we apply 100 US publicly traded financial institutions and 6 macro state variables to estimate this index. Previously we have used 200 financial institutions, after comparison we find out that using 100 firms is more efficient way.

Risk Level Explained:

The risks are ordered into different levels with a different color code each. ✗ denotes the current risk level.


severe risk

Red: severe risk of a crisis in the financial market. Our risk measure suggests that a financial crisis is imminent or happening right now. This risk level is given for lambda values higher than the 80%-ratio.


high risk

Orange: high risk of crisis in the financial market. A crisis might occur very soon. This risk level is given for lambda values between the 60%-ratio and 80%-ratio.


elevated risk

Yellow: elevated risk of crisis in the financial market. The incidence of a crisis is somewhat higher than usual. This risk level is given for lambda values between the 40%-ratio and 60%-ratio.


general risk

Blue: general risk of crisis in the financial market. There is no specific risk of a crisis. This risk level is given for lambda values between the 20%-ratio and 40%-ratio.


low risk

Green: low risk of crisis in the financial market. The incidence of a crisis is less likely than usual. This risk level is given for lambda values lower then the 20%-ratio.


Ideas, papers, theory and code used in this project:


FRM Financial Risk Meter (2020)
Advances in Econometrics, The Econometrics of Networks, vol. 42
Mihoci A, Althof M, Chen CYH, Härdle WK

Get Document


An AI approach to Measuring Financial Risk (2020)
Singapore Economic Review
Yu L, Härdle WK, Borke L, Benschop T

Get Document


LASSO-Driven Inference in Time and Space (2018)
IRTG 1792 Discussion Paper 2018-021
Victor Chernoyhukov, Wolfgang Karl Härdle, Chen Huang, Weining Wang

Get Document


Single-Index-Based CoVaR With Very High-Dimensional Covariates (2017)
Journal of Business&Economic Statistics
Yan Fan, Wolfgang Karl Härdle, Weining Wang and Lixing Zhu

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TENET: Tail-Event driven NETwork risk (2016)
Journal of Econometrics
Wolfgang Karl Härdle, Weining Wang, Lining Yu

Get Document


QuantNet is designed as a web-interface to freely exchange numerical methods, called Quantlets


R code for FRM
Search for FRM in QuantNet

Who We Are

Theoretical framework, scientific advice,
Quantile Lasso Regression algorithms

Michael Althof

PhD student,
Humboldt-Universität zu Berlin

Theoretical core driver,
scientific advice

Cathy Yi-hsuan Chen

Professor Ladislaus von Bortkiewicz Lehrstuhl für Statistik, Humboldt-Universität zu Berlin

Theoretical framework,
Expectile Lasso Regression Algorithms

Rui Ren

Post Doc,
Humboldt-Universität zu Berlin

Initiator and key driving force,
theoretical core driver, scientific advice

Wolfgang Karl Härdle

Professor Ladislaus von Bortkiewicz Lehrstuhl für Statistik, Humboldt-Universität zu Berlin

Theoretical core driver,
Research training, development of algorithms and
scientific advice

Andrija Mihoci

Professor and Appointed Lecturer in Statistics, Econometrics and Quantitative Methods,
Brandenburg University of Technology Cottbus-Senftenberg

Theoretical framework,
Quantile Lasso Regression algorithms,
scientific advice

Weining Wang

Hermann-Otto-Hirschfeld Professor,
Humboldt-Universität zu Berlin

Impressum / Imprint


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